This MLS-C01 practice question tests your understanding of data engineering. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company wants to perform real-time analytics on streaming data from clickstreams. The data needs to be ingested, processed, and made available for querying within seconds. Which AWS service should be used for the processing step?
AWS Glue
Why wrong: Glue is primarily batch ETL.
Amazon Redshift
Why wrong: Redshift is not a real-time processing engine.
Amazon Kinesis Data Analytics
Kinesis Data Analytics processes streaming data in real-time.
Amazon Athena
Why wrong: Athena is not designed for real-time streaming.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon Kinesis Data Analytics
Amazon Kinesis Data Analytics is the correct choice because it enables real-time processing and analysis of streaming data using SQL or Apache Flink. It can ingest data from Kinesis Data Streams or Kinesis Data Firehose, process it with sub-second latency, and output results to destinations like Kinesis Data Streams or Firehose for further querying, meeting the requirement for analytics within seconds.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
AWS Glue
Why it's wrong here
Glue is primarily batch ETL.
Amazon Redshift
Why it's wrong here
Redshift is not a real-time processing engine.
Amazon Kinesis Data Analytics
Why this is correct
Kinesis Data Analytics processes streaming data in real-time.
Related concept
Read the scenario before looking for a memorised answer.
Amazon Athena
Why it's wrong here
Athena is not designed for real-time streaming.
Common exam traps
The trap here is that candidates often confuse AWS Glue's streaming ETL capability (which still relies on Spark Structured Streaming with higher latency) with Kinesis Data Analytics' native real-time processing, or they assume Athena can query streaming data directly when it only queries data at rest in S3.
Detailed technical explanation
Kinesis Data Analytics uses Apache Flink under the hood, which provides exactly-once processing semantics and event-time processing for handling out-of-order data in streams. In a real-world clickstream scenario, it can aggregate user sessions in sliding windows (e.g., 5-second tumbling windows) and output metrics like page views per second to a Kinesis Data Stream, which can then be consumed by a dashboard or stored in S3 via Firehose for historical analysis.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon Kinesis Data Analytics — Amazon Kinesis Data Analytics is the correct choice because it enables real-time processing and analysis of streaming data using SQL or Apache Flink. It can ingest data from Kinesis Data Streams or Kinesis Data Firehose, process it with sub-second latency, and output results to destinations like Kinesis Data Streams or Firehose for further querying, meeting the requirement for analytics within seconds.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
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